Ion channel genes represent 1.5% of the human genome, and inherited mutations of these genes elicit a diverse array of clinical disorders of brain, nerve, muscle and heart. In brain, single gene channelopathies are the predominant cause (13/14) of rare mendelian idiopathic epilepsy syndromes, but their contribution to common sporadic epilepsy is unknown. High rates of de novo mutation and complex polygenic inheritance (the "common disease-common variant" model) are two attractive explanations for the role of ion channel variation in sporadic cases. Together with their important pathogenic role, ion channels are also the primary molecular targets of most antiepileptic drugs, and genetic variation in channel subunits may independently contribute to pharmacoresistance. This project combines basic and clinical research on ion channelopathy and specific epilepsy phenotypes with the large scale gene sequencing capacity and mutation analysis resources of the Baylor Human Genome Sequencing Center in order to test the general hypothesis that profiling the coding sequences of large numbers of channel genes in individual epilepsy patients can reveal novel mutations and patterns of common allelic variants that determine epilepsy susceptibility and pharmacoresistance. We will complete the development and optimization of a multiplex primer array that allows rapid and scalable parallel exon sequencing of 100 candidate ion channel genes in 500 patients with specific clinical epilepsy phenotypes and in 500 ethnically-matched controls. A public database of human ion channel gene variation will be generated to facilitate data-sharing. These data will be used in two ways. First, the biophysical and pharmacological properties of a subset of channel gene polymorphisms with predicted protein coding variation will be analyzed in mammalian expression systems in order to define a validated subset of functional gene variants of human ion channels relevant to epilepsy. This list is essential to examine models relating specific pathophysiological properties of ion channels to the patterns associated with epilepsy. Second, the sequence of the 100 channel genes will be assembled into a profile of each individual (their "channotype") and used to test the statistical association of different channotypes with epilepsy phenotypes. Preliminary analysis of all exons of 7 channel genes in 50 patients and 50 controls has detected novel and previously reported SNPs (coding and non-coding) and microdeletions, validating the efficiency of the data collection pipeline. Using robotic processing and automated mutation detection algorithms, we will scale the number of genes and patients to attain the statistical power to address the channotype-phenotype association hypotheses. The associations identified in this study will address a major hypothesis underlying the complex genetics of epilepsy, accelerate development of individualized clinical risk assessments for epilepsy, and examine a novel mechanism of resistance to antiepileptic drugs in children and adults with common idiopathic forms of the disorder.